Designing a wearable MMG-based mobile app for gait rehab

Movement disorder therapies involving sonography and rhythmic entrainment have shown lasting improvements to gait dynamics. Although optimal parameters for gait training have yet to be defined, past studies have shown that increasing training frequency enhances neural reorganization, thus supporting the development of wearable technologies in gait rehabilitation. This paper presents a novel tool for the acquisition of muscle activity, their analysis, and presentation as a live biofeedback signal that distinguishes between typical and atypical gait patterns. Muscle activity is recorded and analyzed on an Arduino, then sent to an Android for feature detection via Bluetooth. Auditory feedback will be presented as a fixed tempo based on stride rate and an interactive drum kit based on matching gait patterns. By developing a tool that can be used at-home, users will be able to train daily and maintain longer rehabilitation programs, thus encouraging neural reorganization. This mobile app will allow us to improve quality of life by enhancing training outcomes and functional gait dynamics.